Hey everyone! I wanted to share my recent project where I fine-tuned an LLM at home using my custom dataset. I've been exploring the capabilities of LLMs and decided to take a hands-on approach. I used a local instance of the LLaMA 2 model, given its impressive ability with fewer resources.
Here's a bit about my setup: I utilized a high-performance PC with an RTX 3090, 24GB VRAM, and 64GB of RAM. To streamline the fine-tuning process, I relied on Hugging Face's transformers library. My goal was to adapt the model for a niche dataset on historical texts to see how well it could generate contextually relevant content.
Cost-wise, setting this up at home was not as steep as expected. Most of my investments were in hardware, which came out to roughly $3,500 - but I see it as a long-term investment as I can use it for future projects too. The electricity bill was an additional consideration, running about $50/month extra during the training phase.
For anyone considering doing this, I recommend testing on smaller subsets of your data first, to avoid unnecessary expenses, and always monitor your GPU and CPU usage. The results have been promising and incredibly satisfying to achieve without renting expensive GPU servers!
Would love to hear if others have done something similar or if you have tips on cost-saving while running LLMs at home!
Great to hear about your success! I went down a similar path using a smaller setup with an RTX 3060, which actually did pretty well for smaller datasets. Cost-saving wise, besides testing on smaller data subsets, I'd recommend looking into dynamic batching; it really helped optimize resource usage for me.
Have you considered leveraging gradient checkpointing for memory management? It saved me a decent amount of memory when I was fine-tuning an LLM, allowing for longer training runs without hitting the GPU limits. Also, curious if you experienced mode collapse, and if so, how did you address it?
I totally agree with trying out smaller subsets first. I did something similar with fine-tuning a BERT model. Started with a Titan RTX and spent around $2,000 in total. I also found leveraging mixed-precision training helped reduce training time and was easier on power consumption. Have you considered this for LLaMA 2?
I'm curious, have you observed any limitations or bottlenecks with the LLaMA 2 model regarding temporal context in historical texts? I'm considering a similar project but worry about how well these models grasp temporal nuances over extended periods.
I've done something similar with the LLaMA 2 using an RTX 3080 and found it very manageable as well. I totally agree with your point on starting with smaller datasets; it helped me avoid bottlenecks. My electricity costs were slightly higher, about $75/month, but still cheaper than cloud resources!
